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The proliferation of online face images has heightened privacy concerns, as adversaries can exploit facial features for nefarious purposes. While adversarial perturbations have been proposed to safeguard these images, their effectiveness remains questionable. This paper introduces IVORY, a novel adversarial purification method leveraging Diffusion Transformer-based Stable Diffusion 3 model to purify perturbed images and improve facial feature extraction. Evaluated across gender recognition, ethnicity recognition and age group classification tasks with CNNs like VGG16, SENet and MobileNetV3 and vision transformers like SwinFace, IVORY consistently restores classifier performance to near-clean levels in white-box settings, outperforming traditional defenses such as Adversarial Training, DiffPure and IMPRESS. For example, it improved gender recognition accuracy from 37.8% to 96% under the PGD attack for VGG16 and age group classification accuracy from 2.1% to 52.4% under AutoAttack for MobileNetV3. In black-box scenarios, IVORY achieves a 22.8% average accuracy gain. IVORY also reduces SSIM noise by over 50% at 1x resolution and up to 80% at 2x resolution compared to DiffPure. Our analysis further reveals that adversarial perturbations alone do not fully protect against soft-biometric extraction, highlighting the need for comprehensive evaluation frameworks and robust defenses. 

Tor users derive anonymity in part from the size of the Tor user base, but Tor struggles to attract and support more users due to performance limitations. Previous works have proposed modifications to Tor’s path selection algorithm to enhance both performance and security, but many proposals have unintended consequences due to incorporating information related to client location. We instead propose selecting paths using a global view of the network, independent of client location, and we propose doing so with a machine learning classifier to predict the performance of a given path before building a circuit. We show through a variety of simulated and live experimental settings, across different time periods, that this approach can significantly improve performance compared to Tor’s default path selection algorithm and two previously proposed approaches. In addition to evaluating the security of our approach with traditional metrics, we propose a novel anonymity metric that captures information leakage resulting from location-aware path selection, and we show that our path selection approach leaks no more information than the default path selection algorithm. 

Abstract Prior approaches to AS-aware path selection in Tor do not consider node bandwidth or the other characteristics that Tor uses to ensure load balancing and quality of service. Further, since the AS path from the client’s exit to her destination can only be inferred once the destination is known, the prior approaches may have problems constructing circuits in advance, which is important for Tor performance. In this paper, we propose and evaluate DeNASA, a new approach to AS-aware path selection that is destination-naive, in that it does not need to know the client’s destination to pick paths, and that takes advantage of Tor’s circuit selection algorithm. To this end, we first identify the most probable ASes to be traversed by Tor streams. We call this set of ASes the Suspect AS list and find that it consists of eight highest ranking Tier 1 ASes. Then, we test the accuracy of Qiu and Gao AS-level path inference on identifying the presence of these ASes in the path, and we show that inference accuracy is 90%. We develop an AS-aware algorithm called DeNASA that uses Qiu and Gao inference to avoid Suspect ASes. DeNASA reduces Tor stream vulnerability by 74%. We also show that DeNASA has performance similar to Tor. Due to the destination-naive property, time to first byte (TTFB) is close to Tor’s, and due to leveraging Tor’s bandwidth-weighted relay selection, time to last byte (TTLB) is also similar to Tor’s.